Compliance - Applied AI/ML Data Scientist - Associate

Bring your expertise to JPMorgan Chase.  As part of Risk Management and Compliance, you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo and striving to be best-in-class.

As a Data Scientist within our innovative team, you will be tasked with devising and developing Proofs of Concept (POCs) and deployable models using AI/ML techniques, algorithms and other statistical and numerical methods. You will also be responsible for extracting and working with large volumes of data (both structured and unstructured) from multiple sources, transforming it into an analysis-ready format to develop the data pipeline. Additionally, you will be expected to independently formulate methodologies, and quantitative and analytical tasks, from business problems.

Job Responsibilities: 

• Analyze complex/unstructured data to understand the business problem and use case
• Analyze business requirements, design, and develop appropriate methodology; And develop deployable, scalable and effective models/ analytical methods as part of technology managed system or as a self-served application of a business user   
• Work collaboratively and creatively with other data scientists, technology partners, risk professionals, model validation teams, etc. 
• Prepare technical documentation of quantitative models for internal model risk and governance review 

Required qualifications, capabilities, and skills 

• 2+ years of related experience in Python, R or Scala with Bachelor of Science degree in Computer Science, Physical Sciences, Econometrics, Statistics, or other any quantitative discipline.
• Demonstrable theoretical and application knowledge of Machine Learning methods, and/or Statistical Models
• Demonstrable hands-on experience and familiarity with LLM prompt engineering and agentic solution development. 
• Demonstrable hands-on experience and familiarity with the following packages, algorithms, and/or alternatives, including ML Packages (Pandas, Scikit-Learn, XGBoost, catboost, lightgbm, automl, Optuna, Hyperopt), Visualization Packages (Matplotlib, Seaborn, Geopandas), Algorithm (Louvain / Hierarchical Clustering, Label Propagation, Connected Component Analysis, Graph Neural net (Graph Attention Network), Page Rank, Centrality Analysis, Tree based Analysis, Outlier Detection Methods, Zero Shot/ Few Shot learning)
• Hands-on professional experience in software development especially with analytical & computationally intensive systems, digital transformations leveraging cloud technologies (AWS, GCP, Azure, Databricks etc.)
• Experience in developing and operationalization of data pipelines 
• Experience with process, controls and governance of a highly regulated environment 
• Experience of data mining and visualization tools including Tableau, Alteryx, Qlik for generating user interface of business solutions; Assimilating large amounts of data from multiple databases and utilize them for creating actionable outcome; Adhering to a standardized analysis and project methodology; and Documenting quantitative analysis

Preferred qualifications, capabilities, and skills 

• Post graduate degrees such as Master’s Degree, PhD, etc. is preferred
• Working knowledge of C/C#/C++ or others is a plus
• Real life exposure to Agile SDLC, ModelOps and /Or Design Thinking is desirable.
• Familiarity with Natural Language Processing techniques is a plus
• Experience with Large Language Models and Suites is a plus
• Familiarity with graph-based learning and graph Database like, TigerGraph, Neo4j is a plus
•  A self-starter and strong influencing skills with strong communication skills